Overview

Dataset statistics

Number of variables25
Number of observations402031
Missing cells88207
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory221.3 MiB
Average record size in memory577.2 B

Variable types

Numeric15
Text4
DateTime1
Categorical4
Boolean1

Alerts

af_energy is highly overall correlated with af_loudnessHigh correlation
af_loudness is highly overall correlated with af_energyHigh correlation
popularity is highly overall correlated with predicted_popularityHigh correlation
predicted_popularity is highly overall correlated with popularityHigh correlation
rank is highly overall correlated with streamsHigh correlation
streams is highly overall correlated with rankHigh correlation
af_time_signature is highly imbalanced (86.8%)Imbalance
streams has 88188 (21.9%) missing valuesMissing
chart_id has unique valuesUnique
popularity has 64118 (15.9%) zerosZeros
af_key has 46729 (11.6%) zerosZeros
af_instrumentalness has 229419 (57.1%) zerosZeros

Reproduction

Analysis started2024-07-15 01:13:16.477892
Analysis finished2024-07-15 01:14:37.265169
Duration1 minute and 20.79 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

chart_id
Real number (ℝ)

UNIQUE 

Distinct402031
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12998453
Minimum803
Maximum26171075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:37.406419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum803
5-th percentile1472789.5
Q16427568.5
median13027021
Q319530458
95-th percentile24623584
Maximum26171075
Range26170272
Interquartile range (IQR)13102890

Descriptive statistics

Standard deviation7479019.1
Coefficient of variation (CV)0.57537763
Kurtosis-1.224669
Mean12998453
Median Absolute Deviation (MAD)6561651
Skewness0.024745339
Sum5.2257811 × 1012
Variance5.5935726 × 1013
MonotonicityStrictly increasing
2024-07-14T21:14:37.611269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
803 1
 
< 0.1%
17352925 1
 
< 0.1%
17352923 1
 
< 0.1%
17352922 1
 
< 0.1%
17352921 1
 
< 0.1%
17352920 1
 
< 0.1%
17350436 1
 
< 0.1%
17350435 1
 
< 0.1%
17350434 1
 
< 0.1%
17350433 1
 
< 0.1%
Other values (402021) 402021
> 99.9%
ValueCountFrequency (%)
803 1
< 0.1%
804 1
< 0.1%
805 1
< 0.1%
806 1
< 0.1%
807 1
< 0.1%
808 1
< 0.1%
809 1
< 0.1%
810 1
< 0.1%
811 1
< 0.1%
812 1
< 0.1%
ValueCountFrequency (%)
26171075 1
< 0.1%
26171074 1
< 0.1%
26171073 1
< 0.1%
26171071 1
< 0.1%
26171070 1
< 0.1%
26171068 1
< 0.1%
26171067 1
< 0.1%
26171066 1
< 0.1%
26171064 1
< 0.1%
26171063 1
< 0.1%

title
Text

Distinct5080
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size33.9 MiB
2024-07-14T21:14:37.898815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length122
Median length83
Mean length15.494733
Min length1

Characters and Unicode

Total characters6229363
Distinct characters183
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique509 ?
Unique (%)0.1%

Sample

1st rowReggaetón Lento (Bailemos)
2nd rowOtra vez (feat. J Balvin)
3rd rowChantaje (feat. Maluma)
4th rowVente Pa' Ca (feat. Maluma)
5th rowTraicionera
ValueCountFrequency (%)
62987
 
5.3%
feat 45628
 
3.8%
remix 40383
 
3.4%
la 24166
 
2.0%
me 21961
 
1.8%
te 15160
 
1.3%
no 13888
 
1.2%
de 13133
 
1.1%
a 10634
 
0.9%
el 10012
 
0.8%
Other values (5129) 935741
78.4%
2024-07-14T21:14:38.402788image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
791662
 
12.7%
e 559411
 
9.0%
a 531124
 
8.5%
o 366982
 
5.9%
i 322776
 
5.2%
n 258313
 
4.1%
t 231832
 
3.7%
r 226508
 
3.6%
l 198068
 
3.2%
s 181209
 
2.9%
Other values (173) 2561478
41.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6229363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
791662
 
12.7%
e 559411
 
9.0%
a 531124
 
8.5%
o 366982
 
5.9%
i 322776
 
5.2%
n 258313
 
4.1%
t 231832
 
3.7%
r 226508
 
3.6%
l 198068
 
3.2%
s 181209
 
2.9%
Other values (173) 2561478
41.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6229363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
791662
 
12.7%
e 559411
 
9.0%
a 531124
 
8.5%
o 366982
 
5.9%
i 322776
 
5.2%
n 258313
 
4.1%
t 231832
 
3.7%
r 226508
 
3.6%
l 198068
 
3.2%
s 181209
 
2.9%
Other values (173) 2561478
41.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6229363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
791662
 
12.7%
e 559411
 
9.0%
a 531124
 
8.5%
o 366982
 
5.9%
i 322776
 
5.2%
n 258313
 
4.1%
t 231832
 
3.7%
r 226508
 
3.6%
l 198068
 
3.2%
s 181209
 
2.9%
Other values (173) 2561478
41.1%

rank
Real number (ℝ)

HIGH CORRELATION 

Distinct200
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.899617
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:38.611645image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q128
median62
Q3121
95-th percentile183
Maximum200
Range199
Interquartile range (IQR)93

Descriptive statistics

Standard deviation56.935098
Coefficient of variation (CV)0.74038208
Kurtosis-0.92384711
Mean76.899617
Median Absolute Deviation (MAD)41
Skewness0.54223727
Sum30916030
Variance3241.6054
MonotonicityNot monotonic
2024-07-14T21:14:38.832722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3638
 
0.9%
2 3638
 
0.9%
6 3624
 
0.9%
5 3622
 
0.9%
3 3620
 
0.9%
7 3613
 
0.9%
8 3613
 
0.9%
4 3613
 
0.9%
10 3611
 
0.9%
11 3606
 
0.9%
Other values (190) 365833
91.0%
ValueCountFrequency (%)
1 3638
0.9%
2 3638
0.9%
3 3620
0.9%
4 3613
0.9%
5 3622
0.9%
6 3624
0.9%
7 3613
0.9%
8 3613
0.9%
9 3592
0.9%
10 3611
0.9%
ValueCountFrequency (%)
200 1140
0.3%
199 1141
0.3%
198 1144
0.3%
197 1149
0.3%
196 1151
0.3%
195 1153
0.3%
194 1159
0.3%
193 1163
0.3%
192 1163
0.3%
191 1164
0.3%

date
Date

Distinct1826
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
Minimum2017-01-01 00:00:00
Maximum2021-12-31 00:00:00
2024-07-14T21:14:39.067615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:39.274407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

artist
Text

Distinct3267
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size34.2 MiB
2024-07-14T21:14:39.531140image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length421
Median length102
Mean length18.462051
Min length1

Characters and Unicode

Total characters7422317
Distinct characters147
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique230 ?
Unique (%)0.1%

Sample

1st rowCNCO
2nd rowZion & Lennox
3rd rowShakira
4th rowRicky Martin
5th rowSebastian Yatra
ValueCountFrequency (%)
bunny 29398
 
2.4%
bad 29348
 
2.4%
j 24052
 
1.9%
balvin 23747
 
1.9%
ozuna 18286
 
1.5%
sebastian 16200
 
1.3%
yatra 16158
 
1.3%
g 16028
 
1.3%
maluma 14463
 
1.2%
alejandro 14153
 
1.1%
Other values (3566) 1044588
83.8%
2024-07-14T21:14:39.996368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
844390
 
11.4%
a 751016
 
10.1%
e 503412
 
6.8%
n 485450
 
6.5%
i 395387
 
5.3%
o 335845
 
4.5%
, 322342
 
4.3%
r 306537
 
4.1%
l 300188
 
4.0%
u 237799
 
3.2%
Other values (137) 2939951
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7422317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
844390
 
11.4%
a 751016
 
10.1%
e 503412
 
6.8%
n 485450
 
6.5%
i 395387
 
5.3%
o 335845
 
4.5%
, 322342
 
4.3%
r 306537
 
4.1%
l 300188
 
4.0%
u 237799
 
3.2%
Other values (137) 2939951
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7422317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
844390
 
11.4%
a 751016
 
10.1%
e 503412
 
6.8%
n 485450
 
6.5%
i 395387
 
5.3%
o 335845
 
4.5%
, 322342
 
4.3%
r 306537
 
4.1%
l 300188
 
4.0%
u 237799
 
3.2%
Other values (137) 2939951
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7422317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
844390
 
11.4%
a 751016
 
10.1%
e 503412
 
6.8%
n 485450
 
6.5%
i 395387
 
5.3%
o 335845
 
4.5%
, 322342
 
4.3%
r 306537
 
4.1%
l 300188
 
4.0%
u 237799
 
3.2%
Other values (137) 2939951
39.6%

trend
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.3 MiB
MOVE_DOWN
168117 
MOVE_UP
150412 
SAME_POSITION
57643 
NEW_ENTRY
25859 

Length

Max length13
Median length9
Mean length8.8252573
Min length7

Characters and Unicode

Total characters3548027
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSAME_POSITION
2nd rowSAME_POSITION
3rd rowSAME_POSITION
4th rowMOVE_UP
5th rowMOVE_DOWN

Common Values

ValueCountFrequency (%)
MOVE_DOWN 168117
41.8%
MOVE_UP 150412
37.4%
SAME_POSITION 57643
 
14.3%
NEW_ENTRY 25859
 
6.4%

Length

2024-07-14T21:14:40.189387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T21:14:40.342188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
move_down 168117
41.8%
move_up 150412
37.4%
same_position 57643
 
14.3%
new_entry 25859
 
6.4%

Most occurring characters

ValueCountFrequency (%)
O 601932
17.0%
E 427890
12.1%
_ 402031
11.3%
M 376172
10.6%
V 318529
9.0%
N 277478
7.8%
P 208055
 
5.9%
W 193976
 
5.5%
D 168117
 
4.7%
U 150412
 
4.2%
Other values (6) 423435
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3548027
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 601932
17.0%
E 427890
12.1%
_ 402031
11.3%
M 376172
10.6%
V 318529
9.0%
N 277478
7.8%
P 208055
 
5.9%
W 193976
 
5.5%
D 168117
 
4.7%
U 150412
 
4.2%
Other values (6) 423435
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3548027
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 601932
17.0%
E 427890
12.1%
_ 402031
11.3%
M 376172
10.6%
V 318529
9.0%
N 277478
7.8%
P 208055
 
5.9%
W 193976
 
5.5%
D 168117
 
4.7%
U 150412
 
4.2%
Other values (6) 423435
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3548027
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 601932
17.0%
E 427890
12.1%
_ 402031
11.3%
M 376172
10.6%
V 318529
9.0%
N 277478
7.8%
P 208055
 
5.9%
W 193976
 
5.5%
D 168117
 
4.7%
U 150412
 
4.2%
Other values (6) 423435
11.9%

streams
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17907
Distinct (%)5.7%
Missing88188
Missing (%)21.9%
Infinite0
Infinite (%)0.0%
Mean3731.0066
Minimum1001
Maximum81858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:40.576575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1140
Q11796
median2629
Q34142
95-th percentile10155
Maximum81858
Range80857
Interquartile range (IQR)2346

Descriptive statistics

Standard deviation3543.2684
Coefficient of variation (CV)0.94968162
Kurtosis26.978097
Mean3731.0066
Median Absolute Deviation (MAD)1008
Skewness3.9326684
Sum1.1709503 × 109
Variance12554751
MonotonicityNot monotonic
2024-07-14T21:14:40.797777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1021 143
 
< 0.1%
1038 139
 
< 0.1%
1043 134
 
< 0.1%
1033 134
 
< 0.1%
1184 133
 
< 0.1%
1024 133
 
< 0.1%
1019 133
 
< 0.1%
1039 132
 
< 0.1%
1008 131
 
< 0.1%
1010 131
 
< 0.1%
Other values (17897) 312500
77.7%
(Missing) 88188
 
21.9%
ValueCountFrequency (%)
1001 116
< 0.1%
1002 129
< 0.1%
1003 118
< 0.1%
1004 129
< 0.1%
1005 100
< 0.1%
1006 129
< 0.1%
1007 131
< 0.1%
1008 131
< 0.1%
1009 115
< 0.1%
1010 131
< 0.1%
ValueCountFrequency (%)
81858 1
< 0.1%
71779 1
< 0.1%
68209 1
< 0.1%
64255 1
< 0.1%
63590 1
< 0.1%
62765 1
< 0.1%
62750 1
< 0.1%
62483 1
< 0.1%
62437 1
< 0.1%
61086 1
< 0.1%
Distinct6053
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size33.4 MiB
2024-07-14T21:14:40.990610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters8844682
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique681 ?
Unique (%)0.2%

Sample

1st row3AEZUABDXNtecAOSC1qTfo
2nd row3QwBODjSEzelZyVjxPOHdq
3rd row6mICuAdrwEjh6Y6lroV2Kg
4th row7DM4BPaS7uofFul3ywMe46
5th row5J1c3M4EldCfNxXwrwt8mT
ValueCountFrequency (%)
7qizfu4dy1lwllzx7mpbi3 1252
 
0.3%
059bcihyc2sbwm6sw2azzd 1250
 
0.3%
3qwbodjsezelzyvjxpohdq 1196
 
0.3%
0pqnghjpmpxlkifkrmu6wp 1181
 
0.3%
1j6xogusnyxq3l6irykf3g 1155
 
0.3%
7dhyjnlksxzhbrqeheaums 1145
 
0.3%
4r8bjggjostswlxtkw8v7p 1110
 
0.3%
4nut1fcno9aqaelbgxq3kr 1097
 
0.3%
0fea68admynygetgi4rc18 1088
 
0.3%
2th65lnhgvlxckxm3apjxi 1075
 
0.3%
Other values (6043) 390482
97.1%
2024-07-14T21:14:41.419436image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 202965
 
2.3%
2 192148
 
2.2%
3 190146
 
2.1%
4 188939
 
2.1%
0 188245
 
2.1%
6 187266
 
2.1%
7 184563
 
2.1%
5 178010
 
2.0%
w 155010
 
1.8%
q 152055
 
1.7%
Other values (52) 7025335
79.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8844682
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 202965
 
2.3%
2 192148
 
2.2%
3 190146
 
2.1%
4 188939
 
2.1%
0 188245
 
2.1%
6 187266
 
2.1%
7 184563
 
2.1%
5 178010
 
2.0%
w 155010
 
1.8%
q 152055
 
1.7%
Other values (52) 7025335
79.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8844682
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 202965
 
2.3%
2 192148
 
2.2%
3 190146
 
2.1%
4 188939
 
2.1%
0 188245
 
2.1%
6 187266
 
2.1%
7 184563
 
2.1%
5 178010
 
2.0%
w 155010
 
1.8%
q 152055
 
1.7%
Other values (52) 7025335
79.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8844682
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 202965
 
2.3%
2 192148
 
2.2%
3 190146
 
2.1%
4 188939
 
2.1%
0 188245
 
2.1%
6 187266
 
2.1%
7 184563
 
2.1%
5 178010
 
2.0%
w 155010
 
1.8%
q 152055
 
1.7%
Other values (52) 7025335
79.4%

album
Text

Distinct4450
Distinct (%)1.1%
Missing19
Missing (%)< 0.1%
Memory size33.2 MiB
2024-07-14T21:14:41.669026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length122
Median length85
Mean length14.693243
Min length1

Characters and Unicode

Total characters5906860
Distinct characters182
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique370 ?
Unique (%)0.1%

Sample

1st rowPrimera Cita
2nd rowMotivan2
3rd rowEl Dorado
4th rowVente Pa' Ca (feat. Maluma)
5th rowTraicionera
ValueCountFrequency (%)
remix 31649
 
2.9%
feat 29572
 
2.7%
21026
 
1.9%
la 16211
 
1.5%
el 13762
 
1.3%
me 13648
 
1.3%
the 12939
 
1.2%
de 11213
 
1.0%
a 10609
 
1.0%
te 9595
 
0.9%
Other values (4984) 909398
84.2%
2024-07-14T21:14:42.139346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
677610
 
11.5%
e 489412
 
8.3%
a 452205
 
7.7%
o 337196
 
5.7%
i 298053
 
5.0%
n 230693
 
3.9%
r 224765
 
3.8%
t 196180
 
3.3%
l 193703
 
3.3%
s 175534
 
3.0%
Other values (172) 2631509
44.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5906860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
677610
 
11.5%
e 489412
 
8.3%
a 452205
 
7.7%
o 337196
 
5.7%
i 298053
 
5.0%
n 230693
 
3.9%
r 224765
 
3.8%
t 196180
 
3.3%
l 193703
 
3.3%
s 175534
 
3.0%
Other values (172) 2631509
44.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5906860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
677610
 
11.5%
e 489412
 
8.3%
a 452205
 
7.7%
o 337196
 
5.7%
i 298053
 
5.0%
n 230693
 
3.9%
r 224765
 
3.8%
t 196180
 
3.3%
l 193703
 
3.3%
s 175534
 
3.0%
Other values (172) 2631509
44.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5906860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
677610
 
11.5%
e 489412
 
8.3%
a 452205
 
7.7%
o 337196
 
5.7%
i 298053
 
5.0%
n 230693
 
3.9%
r 224765
 
3.8%
t 196180
 
3.3%
l 193703
 
3.3%
s 175534
 
3.0%
Other values (172) 2631509
44.6%

popularity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.175805
Minimum0
Maximum91
Zeros64118
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:42.329789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q149
median65
Q373
95-th percentile82
Maximum91
Range91
Interquartile range (IQR)24

Descriptive statistics

Standard deviation27.880608
Coefficient of variation (CV)0.51463209
Kurtosis-0.21729518
Mean54.175805
Median Absolute Deviation (MAD)10
Skewness-1.1184368
Sum21780353
Variance777.32828
MonotonicityNot monotonic
2024-07-14T21:14:42.518639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64118
 
15.9%
74 16892
 
4.2%
68 14935
 
3.7%
70 13344
 
3.3%
73 12922
 
3.2%
69 12365
 
3.1%
78 12295
 
3.1%
75 11479
 
2.9%
67 11444
 
2.8%
76 11218
 
2.8%
Other values (82) 221019
55.0%
ValueCountFrequency (%)
0 64118
15.9%
1 3281
 
0.8%
2 1681
 
0.4%
3 867
 
0.2%
4 739
 
0.2%
5 926
 
0.2%
6 342
 
0.1%
7 1342
 
0.3%
8 187
 
< 0.1%
9 114
 
< 0.1%
ValueCountFrequency (%)
91 5
 
< 0.1%
90 782
 
0.2%
89 764
 
0.2%
88 2668
0.7%
87 2318
0.6%
86 3966
1.0%
85 2572
0.6%
84 3308
0.8%
83 3182
0.8%
82 2891
0.7%

duration_ms
Real number (ℝ)

Distinct5106
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214391.34
Minimum0
Maximum720000
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:42.739497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile157890
Q1188560
median209180
Q3231848
95-th percentile295176
Maximum720000
Range720000
Interquartile range (IQR)43288

Descriptive statistics

Standard deviation42649.001
Coefficient of variation (CV)0.19893062
Kurtosis4.6928473
Mean214391.34
Median Absolute Deviation (MAD)21752
Skewness1.3293303
Sum8.6191963 × 1010
Variance1.8189373 × 109
MonotonicityNot monotonic
2024-07-14T21:14:42.941923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205714 2063
 
0.5%
204346 1661
 
0.4%
220116 1533
 
0.4%
233712 1305
 
0.3%
231848 1250
 
0.3%
209453 1196
 
0.3%
266160 1145
 
0.3%
309120 1110
 
0.3%
174000 1104
 
0.3%
231773 1097
 
0.3%
Other values (5096) 388567
96.7%
ValueCountFrequency (%)
0 19
< 0.1%
41454 6
 
< 0.1%
46889 2
 
< 0.1%
58149 44
< 0.1%
62422 1
 
< 0.1%
66226 2
 
< 0.1%
67996 1
 
< 0.1%
68205 8
 
< 0.1%
68522 1
 
< 0.1%
69813 40
< 0.1%
ValueCountFrequency (%)
720000 6
 
< 0.1%
621106 4
 
< 0.1%
620101 3
 
< 0.1%
613026 31
< 0.1%
581728 2
 
< 0.1%
544626 5
 
< 0.1%
522000 3
 
< 0.1%
520786 7
 
< 0.1%
512093 1
 
< 0.1%
502186 4
 
< 0.1%

explicit
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
False
320979 
True
81052 
ValueCountFrequency (%)
False 320979
79.8%
True 81052
 
20.2%
2024-07-14T21:14:43.120731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

af_danceability
Real number (ℝ)

Distinct679
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72220811
Minimum0.0783
Maximum0.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:43.260371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0783
5-th percentile0.498
Q10.672
median0.743
Q30.8
95-th percentile0.871
Maximum0.98
Range0.9017
Interquartile range (IQR)0.128

Descriptive statistics

Standard deviation0.11414508
Coefficient of variation (CV)0.15805012
Kurtosis1.61492
Mean0.72220811
Median Absolute Deviation (MAD)0.063
Skewness-1.1031197
Sum290350.05
Variance0.0130291
MonotonicityNot monotonic
2024-07-14T21:14:43.799875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.744 3886
 
1.0%
0.795 3787
 
0.9%
0.76 3681
 
0.9%
0.807 3248
 
0.8%
0.762 3215
 
0.8%
0.728 3134
 
0.8%
0.823 3057
 
0.8%
0.817 2986
 
0.7%
0.826 2905
 
0.7%
0.684 2856
 
0.7%
Other values (669) 369276
91.9%
ValueCountFrequency (%)
0.0783 59
< 0.1%
0.0986 3
 
< 0.1%
0.15 41
< 0.1%
0.161 1
 
< 0.1%
0.168 7
 
< 0.1%
0.179 14
 
< 0.1%
0.181 7
 
< 0.1%
0.201 4
 
< 0.1%
0.207 24
< 0.1%
0.209 4
 
< 0.1%
ValueCountFrequency (%)
0.98 79
< 0.1%
0.967 45
 
< 0.1%
0.964 178
< 0.1%
0.963 26
 
< 0.1%
0.962 7
 
< 0.1%
0.96 4
 
< 0.1%
0.959 8
 
< 0.1%
0.957 13
 
< 0.1%
0.956 55
 
< 0.1%
0.955 25
 
< 0.1%

af_energy
Real number (ℝ)

HIGH CORRELATION 

Distinct805
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68419158
Minimum0.0188
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:44.059108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0188
5-th percentile0.413
Q10.603
median0.709
Q30.79
95-th percentile0.876
Maximum0.999
Range0.9802
Interquartile range (IQR)0.187

Descriptive statistics

Standard deviation0.14587189
Coefficient of variation (CV)0.21320329
Kurtosis0.71377742
Mean0.68419158
Median Absolute Deviation (MAD)0.09
Skewness-0.83707575
Sum275066.23
Variance0.021278609
MonotonicityNot monotonic
2024-07-14T21:14:44.291548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.773 3648
 
0.9%
0.724 3389
 
0.8%
0.859 3199
 
0.8%
0.791 2961
 
0.7%
0.712 2945
 
0.7%
0.672 2749
 
0.7%
0.838 2731
 
0.7%
0.731 2687
 
0.7%
0.745 2550
 
0.6%
0.771 2541
 
0.6%
Other values (795) 372631
92.7%
ValueCountFrequency (%)
0.0188 3
 
< 0.1%
0.0305 2
 
< 0.1%
0.0413 50
< 0.1%
0.0513 2
 
< 0.1%
0.0561 7
 
< 0.1%
0.0568 7
 
< 0.1%
0.0809 9
 
< 0.1%
0.0828 2
 
< 0.1%
0.0829 2
 
< 0.1%
0.0842 12
 
< 0.1%
ValueCountFrequency (%)
0.999 3
 
< 0.1%
0.998 7
 
< 0.1%
0.995 11
 
< 0.1%
0.993 7
 
< 0.1%
0.99 7
 
< 0.1%
0.989 66
< 0.1%
0.988 70
< 0.1%
0.978 18
 
< 0.1%
0.977 7
 
< 0.1%
0.975 6
 
< 0.1%

af_key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3391903
Minimum0
Maximum11
Zeros46729
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:44.455379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.6959745
Coefficient of variation (CV)0.69223503
Kurtosis-1.3346966
Mean5.3391903
Median Absolute Deviation (MAD)3
Skewness-0.009895131
Sum2146520
Variance13.660228
MonotonicityNot monotonic
2024-07-14T21:14:44.663985image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 54685
13.6%
0 46729
11.6%
11 42521
10.6%
6 38011
9.5%
7 36158
9.0%
9 34166
8.5%
5 33362
8.3%
8 30727
7.6%
2 30320
7.5%
10 25056
6.2%
Other values (2) 30296
7.5%
ValueCountFrequency (%)
0 46729
11.6%
1 54685
13.6%
2 30320
7.5%
3 9572
 
2.4%
4 20724
 
5.2%
5 33362
8.3%
6 38011
9.5%
7 36158
9.0%
8 30727
7.6%
9 34166
8.5%
ValueCountFrequency (%)
11 42521
10.6%
10 25056
6.2%
9 34166
8.5%
8 30727
7.6%
7 36158
9.0%
6 38011
9.5%
5 33362
8.3%
4 20724
5.2%
3 9572
 
2.4%
2 30320
7.5%

af_loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct4071
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.2027539
Minimum-30.663
Maximum1.509
Zeros0
Zeros (%)0.0%
Negative401969
Negative (%)> 99.9%
Memory size6.1 MiB
2024-07-14T21:14:44.978619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-30.663
5-th percentile-9.096
Q1-6.169
median-4.791
Q3-3.855
95-th percentile-2.657
Maximum1.509
Range32.172
Interquartile range (IQR)2.314

Descriptive statistics

Standard deviation2.1079685
Coefficient of variation (CV)-0.40516399
Kurtosis5.7531305
Mean-5.2027539
Median Absolute Deviation (MAD)1.123
Skewness-1.6784627
Sum-2091668.4
Variance4.4435314
MonotonicityNot monotonic
2024-07-14T21:14:45.175977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.327 1859
 
0.5%
-4.323 1789
 
0.4%
-7.125 1759
 
0.4%
-5.119 1629
 
0.4%
-4.773 1503
 
0.4%
-4.803 1481
 
0.4%
-3.183 1307
 
0.3%
-4.246 1250
 
0.3%
-5.429 1197
 
0.3%
-4.374 1181
 
0.3%
Other values (4061) 387076
96.3%
ValueCountFrequency (%)
-30.663 2
 
< 0.1%
-27.666 5
< 0.1%
-26.574 4
< 0.1%
-26.129 2
 
< 0.1%
-23.597 3
< 0.1%
-23.274 3
< 0.1%
-23.128 7
< 0.1%
-23.023 7
< 0.1%
-22.867 3
< 0.1%
-22.602 5
< 0.1%
ValueCountFrequency (%)
1.509 7
 
< 0.1%
0.302 17
 
< 0.1%
0.175 38
 
< 0.1%
-0.02 13
 
< 0.1%
-0.515 277
0.1%
-0.521 25
 
< 0.1%
-0.582 104
 
< 0.1%
-0.604 20
 
< 0.1%
-0.739 119
< 0.1%
-0.776 13
 
< 0.1%

af_mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.1 MiB
1.0
236472 
0.0
165559 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1206093
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 236472
58.8%
0.0 165559
41.2%

Length

2024-07-14T21:14:45.336219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T21:14:45.454501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 236472
58.8%
0.0 165559
41.2%

Most occurring characters

ValueCountFrequency (%)
0 567590
47.1%
. 402031
33.3%
1 236472
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1206093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 567590
47.1%
. 402031
33.3%
1 236472
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1206093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 567590
47.1%
. 402031
33.3%
1 236472
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1206093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 567590
47.1%
. 402031
33.3%
1 236472
19.6%

af_speechiness
Real number (ℝ)

Distinct1071
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11146009
Minimum0.0231
Maximum0.884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:45.786223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0231
5-th percentile0.0325
Q10.0512
median0.0779
Q30.14
95-th percentile0.312
Maximum0.884
Range0.8609
Interquartile range (IQR)0.0888

Descriptive statistics

Standard deviation0.087719262
Coefficient of variation (CV)0.78700152
Kurtosis2.7127404
Mean0.11146009
Median Absolute Deviation (MAD)0.0343
Skewness1.6771008
Sum44810.412
Variance0.0076946689
MonotonicityNot monotonic
2024-07-14T21:14:46.202426image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.113 2481
 
0.6%
0.103 2432
 
0.6%
0.1 2398
 
0.6%
0.0785 2219
 
0.6%
0.154 2210
 
0.5%
0.111 2178
 
0.5%
0.0432 2086
 
0.5%
0.2 2064
 
0.5%
0.109 2028
 
0.5%
0.129 2018
 
0.5%
Other values (1061) 379917
94.5%
ValueCountFrequency (%)
0.0231 7
 
< 0.1%
0.0232 822
0.2%
0.0235 79
 
< 0.1%
0.0236 9
 
< 0.1%
0.024 37
 
< 0.1%
0.0243 562
0.1%
0.0245 7
 
< 0.1%
0.0247 20
 
< 0.1%
0.0248 1
 
< 0.1%
0.0249 17
 
< 0.1%
ValueCountFrequency (%)
0.884 11
 
< 0.1%
0.777 41
< 0.1%
0.733 3
 
< 0.1%
0.717 1
 
< 0.1%
0.649 7
 
< 0.1%
0.607 4
 
< 0.1%
0.594 14
 
< 0.1%
0.556 37
< 0.1%
0.547 37
< 0.1%
0.53 26
< 0.1%

af_acousticness
Real number (ℝ)

Distinct1929
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23930709
Minimum6.11 × 10-6
Maximum0.995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:46.436682image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum6.11 × 10-6
5-th percentile0.0132
Q10.0728
median0.178
Q30.346
95-th percentile0.689
Maximum0.995
Range0.99499389
Interquartile range (IQR)0.2732

Descriptive statistics

Standard deviation0.21324725
Coefficient of variation (CV)0.89110292
Kurtosis1.1079483
Mean0.23930709
Median Absolute Deviation (MAD)0.1232
Skewness1.2463239
Sum96208.868
Variance0.045474388
MonotonicityNot monotonic
2024-07-14T21:14:46.652272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.176 3479
 
0.9%
0.186 2897
 
0.7%
0.237 2803
 
0.7%
0.323 2443
 
0.6%
0.125 2374
 
0.6%
0.161 2370
 
0.6%
0.175 2361
 
0.6%
0.11 2262
 
0.6%
0.39 2007
 
0.5%
0.14 1972
 
0.5%
Other values (1919) 377063
93.8%
ValueCountFrequency (%)
6.11 × 10-62
 
< 0.1%
6.52 × 10-61
 
< 0.1%
1.05 × 10-53
 
< 0.1%
2.67 × 10-57
 
< 0.1%
2.95 × 10-514
 
< 0.1%
3.12 × 10-51
 
< 0.1%
3.62 × 10-52
 
< 0.1%
3.84 × 10-5173
< 0.1%
4.16 × 10-519
 
< 0.1%
5.54 × 10-514
 
< 0.1%
ValueCountFrequency (%)
0.995 5
 
< 0.1%
0.992 4
 
< 0.1%
0.991 9
 
< 0.1%
0.99 12
 
< 0.1%
0.989 40
 
< 0.1%
0.988 3
 
< 0.1%
0.987 2
 
< 0.1%
0.986 9
 
< 0.1%
0.985 435
0.1%
0.984 332
0.1%

af_instrumentalness
Real number (ℝ)

ZEROS 

Distinct1898
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0077434446
Minimum0
Maximum0.981
Zeros229419
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:46.854287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.83 × 10-5
95-th percentile0.00375
Maximum0.981
Range0.981
Interquartile range (IQR)1.83 × 10-5

Descriptive statistics

Standard deviation0.064890654
Coefficient of variation (CV)8.3800759
Kurtosis126.19733
Mean0.0077434446
Median Absolute Deviation (MAD)0
Skewness10.825889
Sum3113.1048
Variance0.004210797
MonotonicityNot monotonic
2024-07-14T21:14:47.081356image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 229419
57.1%
1.85 × 10-51406
 
0.3%
2.12 × 10-61402
 
0.3%
1.14 × 10-61278
 
0.3%
3.46 × 10-51250
 
0.3%
1.58 × 10-61207
 
0.3%
0.000486 1196
 
0.3%
1.02 × 10-51133
 
0.3%
2.02 × 10-51043
 
0.3%
4.21 × 10-61041
 
0.3%
Other values (1888) 161656
40.2%
ValueCountFrequency (%)
0 229419
57.1%
1 × 10-6242
 
0.1%
1.01 × 10-662
 
< 0.1%
1.02 × 10-6302
 
0.1%
1.03 × 10-66
 
< 0.1%
1.04 × 10-628
 
< 0.1%
1.05 × 10-616
 
< 0.1%
1.06 × 10-627
 
< 0.1%
1.07 × 10-6127
 
< 0.1%
1.08 × 10-658
 
< 0.1%
ValueCountFrequency (%)
0.981 3
 
< 0.1%
0.975 1
 
< 0.1%
0.97 1
 
< 0.1%
0.964 7
 
< 0.1%
0.961 45
< 0.1%
0.953 11
 
< 0.1%
0.951 4
 
< 0.1%
0.95 2
 
< 0.1%
0.949 6
 
< 0.1%
0.948 2
 
< 0.1%

af_liveness
Real number (ℝ)

Distinct1121
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16355626
Minimum0.019
Maximum0.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:47.250548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.019
5-th percentile0.0574
Q10.0897
median0.118
Q30.195
95-th percentile0.386
Maximum0.98
Range0.961
Interquartile range (IQR)0.1053

Descriptive statistics

Standard deviation0.12273211
Coefficient of variation (CV)0.7503969
Kurtosis7.2752239
Mean0.16355626
Median Absolute Deviation (MAD)0.037
Skewness2.3523927
Sum65754.686
Variance0.015063171
MonotonicityNot monotonic
2024-07-14T21:14:47.424085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.106 6347
 
1.6%
0.101 5415
 
1.3%
0.103 5056
 
1.3%
0.108 4474
 
1.1%
0.102 4399
 
1.1%
0.107 3922
 
1.0%
0.104 3908
 
1.0%
0.123 3777
 
0.9%
0.11 3776
 
0.9%
0.128 3757
 
0.9%
Other values (1111) 357200
88.8%
ValueCountFrequency (%)
0.019 7
 
< 0.1%
0.0202 15
 
< 0.1%
0.0207 171
< 0.1%
0.0215 106
< 0.1%
0.0219 24
 
< 0.1%
0.024 177
< 0.1%
0.0243 13
 
< 0.1%
0.0247 2
 
< 0.1%
0.0251 65
 
< 0.1%
0.0258 4
 
< 0.1%
ValueCountFrequency (%)
0.98 2
 
< 0.1%
0.978 61
< 0.1%
0.976 6
 
< 0.1%
0.974 2
 
< 0.1%
0.971 2
 
< 0.1%
0.968 22
 
< 0.1%
0.963 3
 
< 0.1%
0.962 7
 
< 0.1%
0.961 18
 
< 0.1%
0.96 5
 
< 0.1%

af_valence
Real number (ℝ)

Distinct935
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61697346
Minimum0.032
Maximum0.982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:47.598505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.032
5-th percentile0.215
Q10.478
median0.652
Q30.78
95-th percentile0.911
Maximum0.982
Range0.95
Interquartile range (IQR)0.302

Descriptive statistics

Standard deviation0.20867749
Coefficient of variation (CV)0.33822767
Kurtosis-0.47601676
Mean0.61697346
Median Absolute Deviation (MAD)0.144
Skewness-0.51557593
Sum248042.46
Variance0.043546297
MonotonicityNot monotonic
2024-07-14T21:14:47.763470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.68 3026
 
0.8%
0.839 2415
 
0.6%
0.761 2377
 
0.6%
0.706 2219
 
0.6%
0.695 2153
 
0.5%
0.637 2085
 
0.5%
0.619 2075
 
0.5%
0.78 2062
 
0.5%
0.71 2000
 
0.5%
0.618 1980
 
0.5%
Other values (925) 379639
94.4%
ValueCountFrequency (%)
0.032 15
 
< 0.1%
0.0348 5
 
< 0.1%
0.036 45
< 0.1%
0.0378 13
 
< 0.1%
0.038 31
< 0.1%
0.0386 3
 
< 0.1%
0.0392 19
 
< 0.1%
0.0393 64
< 0.1%
0.0395 5
 
< 0.1%
0.0398 51
< 0.1%
ValueCountFrequency (%)
0.982 11
 
< 0.1%
0.98 14
 
< 0.1%
0.978 1
 
< 0.1%
0.976 10
 
< 0.1%
0.975 7
 
< 0.1%
0.974 7
 
< 0.1%
0.973 136
< 0.1%
0.971 2
 
< 0.1%
0.969 2
 
< 0.1%
0.968 75
< 0.1%

af_tempo
Real number (ℝ)

Distinct4930
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.73679
Minimum48.936
Maximum214.131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 MiB
2024-07-14T21:14:47.933385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum48.936
5-th percentile83.836
Q194.059
median109.326
Q3160.007
95-th percentile180.073
Maximum214.131
Range165.195
Interquartile range (IQR)65.948

Descriptive statistics

Standard deviation34.804272
Coefficient of variation (CV)0.28127666
Kurtosis-1.1600029
Mean123.73679
Median Absolute Deviation (MAD)18.326
Skewness0.55749643
Sum49746027
Variance1211.3373
MonotonicityNot monotonic
2024-07-14T21:14:48.107529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104.823 1859
 
0.5%
170.019 1533
 
0.4%
96.029 1533
 
0.4%
98.005 1449
 
0.4%
95.977 1328
 
0.3%
174.006 1313
 
0.3%
94.983 1311
 
0.3%
102.02 1250
 
0.3%
96.016 1196
 
0.3%
124.949 1181
 
0.3%
Other values (4920) 388078
96.5%
ValueCountFrequency (%)
48.936 39
 
< 0.1%
57.967 7
 
< 0.1%
62.009 1
 
< 0.1%
62.426 7
 
< 0.1%
62.484 10
 
< 0.1%
62.52 6
 
< 0.1%
62.948 7
 
< 0.1%
64.177 42
 
< 0.1%
64.921 175
< 0.1%
64.934 143
< 0.1%
ValueCountFrequency (%)
214.131 7
 
< 0.1%
212.117 147
< 0.1%
212.058 7
 
< 0.1%
211.974 13
 
< 0.1%
209.733 1
 
< 0.1%
209.47 1
 
< 0.1%
209.08 2
 
< 0.1%
208.902 1
 
< 0.1%
207.923 1
 
< 0.1%
207.798 9
 
< 0.1%

af_time_signature
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.1 MiB
4.0
387519 
3.0
 
10351
5.0
 
3013
1.0
 
1148

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1206093
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 387519
96.4%
3.0 10351
 
2.6%
5.0 3013
 
0.7%
1.0 1148
 
0.3%

Length

2024-07-14T21:14:48.249135image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T21:14:48.359436image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
4.0 387519
96.4%
3.0 10351
 
2.6%
5.0 3013
 
0.7%
1.0 1148
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 402031
33.3%
0 402031
33.3%
4 387519
32.1%
3 10351
 
0.9%
5 3013
 
0.2%
1 1148
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1206093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 402031
33.3%
0 402031
33.3%
4 387519
32.1%
3 10351
 
0.9%
5 3013
 
0.2%
1 1148
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1206093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 402031
33.3%
0 402031
33.3%
4 387519
32.1%
3 10351
 
0.9%
5 3013
 
0.2%
1 1148
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1206093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 402031
33.3%
0 402031
33.3%
4 387519
32.1%
3 10351
 
0.9%
5 3013
 
0.2%
1 1148
 
0.1%

predicted_popularity
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.3 MiB
1
298641 
0
103390 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters402031
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 298641
74.3%
0 103390
 
25.7%

Length

2024-07-14T21:14:48.487766image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-14T21:14:48.606015image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 298641
74.3%
0 103390
 
25.7%

Most occurring characters

ValueCountFrequency (%)
1 298641
74.3%
0 103390
 
25.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 402031
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 298641
74.3%
0 103390
 
25.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 402031
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 298641
74.3%
0 103390
 
25.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 402031
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 298641
74.3%
0 103390
 
25.7%

Interactions

2024-07-14T21:14:29.955836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:47.110537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:52.201460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:56.020739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:59.776695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:02.272622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:05.155533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:07.804397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:10.731510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:13.129542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:15.699614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:18.402417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:21.350573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:24.103441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:26.879451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:30.225582image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:47.629227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:52.422221image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:56.528214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:59.930788image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:02.505997image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:05.322246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:07.978707image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:10.923288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:13.298767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:15.873497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:18.563905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:21.542598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:24.271566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:27.106599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:30.479520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:48.174554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:52.660462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:56.898372image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:00.098330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:02.702359image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:05.497273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:08.242768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:11.089314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:13.457374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:16.049375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:18.734321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:21.747308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:24.467617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:27.357408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:30.690666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:48.703758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:52.873574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:57.262382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:00.267718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:02.898982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:05.701328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:08.498187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:11.255425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:13.644289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:16.409333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:18.978362image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:21.956744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:24.677800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:27.797712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:30.895943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:49.120991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:53.112286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:57.492273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:00.412122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:03.120525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:05.871511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:08.777329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:11.399405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:13.801273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:16.581394image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:19.179362image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:22.142667image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:24.867797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:27.965946image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:31.179058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:49.534649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:53.371710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:13:57.769415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:00.591387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:03.310405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:06.042531image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:09.024624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:11.575939image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:13.976671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:16.760392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:19.377301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:22.313691image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-07-14T21:14:18.219567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-07-14T21:14:26.699458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-14T21:14:29.753333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-14T21:14:48.720234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
af_acousticnessaf_danceabilityaf_energyaf_instrumentalnessaf_keyaf_livenessaf_loudnessaf_modeaf_speechinessaf_tempoaf_time_signatureaf_valencechart_idduration_msexplicitpopularitypredicted_popularityrankstreamstrend
af_acousticness1.000-0.036-0.382-0.0300.0070.049-0.1860.076-0.025-0.0140.144-0.0420.0320.0350.0840.0760.0850.0370.0340.026
af_danceability-0.0361.0000.0340.1370.027-0.1100.1320.1020.173-0.2430.2620.251-0.039-0.0330.133-0.0210.078-0.0410.1110.051
af_energy-0.3820.0341.000-0.019-0.0040.0250.6080.1260.044-0.0190.1790.331-0.0850.0600.162-0.1260.104-0.056-0.0120.044
af_instrumentalness-0.0300.137-0.0191.0000.025-0.039-0.0720.0600.0320.0480.040-0.0950.083-0.0260.0510.0460.058-0.0400.0570.022
af_key0.0070.027-0.0040.0251.000-0.0210.0180.2840.038-0.0210.078-0.006-0.0230.0020.0840.0100.078-0.0070.0140.019
af_liveness0.049-0.1100.025-0.039-0.0211.000-0.0390.056-0.060-0.0120.069-0.037-0.002-0.0110.079-0.0500.0530.009-0.0550.017
af_loudness-0.1860.1320.608-0.0720.018-0.0391.0000.0380.0770.0110.0970.349-0.057-0.0260.083-0.0770.073-0.0540.1410.051
af_mode0.0760.1020.1260.0600.2840.0560.0381.000-0.0970.0470.070-0.0450.0010.0180.0590.0340.0280.041-0.0160.012
af_speechiness-0.0250.1730.0440.0320.038-0.0600.077-0.0971.0000.1790.0800.029-0.023-0.0320.137-0.0790.061-0.0670.1400.026
af_tempo-0.014-0.243-0.0190.048-0.021-0.0120.0110.0470.1791.0000.120-0.0290.025-0.0050.1290.0170.0940.0210.0220.049
af_time_signature0.1440.2620.1790.0400.0780.0690.0970.0700.0800.1201.0000.105-0.059-0.0080.058-0.0460.0310.012-0.0230.008
af_valence-0.0420.2510.331-0.095-0.006-0.0370.349-0.0450.029-0.0290.1051.000-0.019-0.0520.166-0.0490.092-0.0170.1060.038
chart_id0.032-0.039-0.0850.083-0.023-0.002-0.0570.001-0.0230.025-0.059-0.0191.000-0.0750.1110.0400.223-0.1460.3060.040
duration_ms0.035-0.0330.060-0.0260.002-0.011-0.0260.018-0.032-0.005-0.008-0.052-0.0751.0000.1960.0510.0470.012-0.0680.017
explicit0.0840.1330.1620.0510.0840.0790.0830.0590.1370.1290.0580.1660.1110.1961.0000.0630.019-0.0200.0870.019
popularity0.076-0.021-0.1260.0460.010-0.050-0.0770.034-0.0790.017-0.046-0.0490.0400.0510.0631.0000.9490.1380.1090.058
predicted_popularity0.0850.0780.1040.0580.0780.0530.0730.0280.0610.0940.0310.0920.2230.0470.0190.9491.0000.1300.1660.056
rank0.037-0.041-0.056-0.040-0.0070.009-0.0540.041-0.0670.0210.012-0.017-0.1460.012-0.0200.1380.1301.000-0.6980.275
streams0.0340.111-0.0120.0570.014-0.0550.141-0.0160.1400.022-0.0230.1060.306-0.0680.0870.1090.166-0.6981.0000.199
trend0.0260.0510.0440.0220.0190.0170.0510.0120.0260.0490.0080.0380.0400.0170.0190.0580.0560.2750.1991.000

Missing values

2024-07-14T21:14:33.393524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-14T21:14:34.846619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-14T21:14:36.703722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

chart_idtitlerankdateartisttrendstreamstrack_idalbumpopularityduration_msexplicitaf_danceabilityaf_energyaf_keyaf_loudnessaf_modeaf_speechinessaf_acousticnessaf_instrumentalnessaf_livenessaf_valenceaf_tempoaf_time_signaturepredicted_popularity
0803Reggaetón Lento (Bailemos)12017-01-01CNCOSAME_POSITION6784.03AEZUABDXNtecAOSC1qTfoPrimera Cita73.0222560.0False0.7610.8384.0-3.0730.00.05020.400000.0000000.17600.71093.9744.01
1804Otra vez (feat. J Balvin)22017-01-01Zion & LennoxSAME_POSITION5748.03QwBODjSEzelZyVjxPOHdqMotivan274.0209453.0False0.8320.77210.0-5.4291.00.10000.055900.0004860.44000.70496.0164.01
2805Chantaje (feat. Maluma)32017-01-01ShakiraSAME_POSITION5506.06mICuAdrwEjh6Y6lroV2KgEl Dorado78.0195840.0False0.8520.7738.0-2.9210.00.07760.187000.0000300.15900.907102.0344.01
3806Vente Pa' Ca (feat. Maluma)42017-01-01Ricky MartinMOVE_UP4804.07DM4BPaS7uofFul3ywMe46Vente Pa' Ca (feat. Maluma)72.0259195.0False0.6630.92011.0-4.0700.00.22600.004310.0000170.10100.53399.9354.01
4807Traicionera52017-01-01Sebastian YatraMOVE_DOWN4780.05J1c3M4EldCfNxXwrwt8mTTraicionera0.0228466.0False0.7760.66911.0-4.9331.00.06380.142000.0000000.21900.66191.0124.00
5808Safari62017-01-01J Balvin, Pharrell Williams, BIA, SkySAME_POSITION4399.06rQSrBHf7HlZjtcMZ4S4bOEnergía0.0205600.0False0.5080.6870.0-4.3611.00.32600.551000.0000030.12600.555180.0444.00
6809Let Me Love You72017-01-01DJ Snake, Justin BieberSAME_POSITION3542.04pdPtRcBmOSQDlJ3Fk945mEncore2.0205946.0False0.4760.7188.0-5.3091.00.05760.078400.0000100.12200.142199.8644.00
7810La Bicicleta82017-01-01Carlos Vives, ShakiraMOVE_UP3507.00sXvAOmXgjR2QUqLK1MltUEl Dorado59.0227706.0False0.7360.9640.0-2.1471.00.12900.198000.0000020.33600.953179.9354.01
8811Ay Mi Dios (feat. Pitbull, Yandel & Chacal)92017-01-01IAmChino, El ChacalMOVE_DOWN3446.06stYbAJgTszHAHZMPxWWCYAy Mi Dios (feat. Pitbull, Yandel & Chacal)0.0252003.0False0.7610.8290.0-3.2030.00.06810.167000.0000000.18900.81392.0334.00
9812Me llamas (feat. Maluma) - Remix102017-01-01Piso 21SAME_POSITION2979.05hEM0JchdVzQ5PwvSfITeXMe Llamas (feat. Maluma) [Remix]69.0210322.0False0.7600.8387.0-3.8280.00.05290.547000.0000010.06640.74593.0504.01
chart_idtitlerankdateartisttrendstreamstrack_idalbumpopularityduration_msexplicitaf_danceabilityaf_energyaf_keyaf_loudnessaf_modeaf_speechinessaf_acousticnessaf_instrumentalnessaf_livenessaf_valenceaf_tempoaf_time_signaturepredicted_popularity
40450026171063Poblado - Remix382021-07-31J Balvin, KAROL G, Nicky Jam, Crissin, Totoy El Frio, Natan & ShanderMOVE_DOWNNaN1WedZeiezCmCEOzLwhx0hVPoblado (Remix)68.0393280.0False0.8130.8093.0-5.3820.00.08460.102000.0000010.37700.64693.0054.01
40450126171064Fulanito392021-07-31Becky G, El AlfaMOVE_UPNaN59L8x0xy8njj75vzVCPMqgFULANITO62.0158531.0False0.9300.8377.0-4.6320.00.18700.256000.0000020.23900.821111.8984.01
40450326171066Fairytale412021-07-31Alexander RybakNEW_ENTRYNaN5D2L026JQTKC2gcq3qkYpjFairytales0.0183093.0False0.5870.7152.0-6.4140.00.02710.434000.0000180.32000.589107.9644.00
40450426171067Love Tonight422021-07-31ShouseNEW_ENTRYNaN1u73tmG4xQschbK8cXxSD9Love Tonight61.0493795.0False0.7960.5520.0-7.2261.00.03140.001130.0101000.08760.468123.0044.01
40450526171068Space Song432021-07-31Beach HouseMOVE_DOWNNaN7H0ya83CMmgFcOhw0UB6owDepression Cherry80.0320466.0False0.5080.7920.0-7.3110.00.02970.229000.1240000.14500.601147.0674.01
40450726171070In Da Getto452021-07-31J Balvin, SkrillexNEW_ENTRYNaN63aj87TQG6F3RVO5nbG2VQIn Da Getto68.0131065.0False0.9150.7208.0-3.1261.00.04590.025000.0000140.09420.631126.9864.01
40450826171071Bad Habits462021-07-31Ed SheeranNEW_ENTRYNaN6PQ88X9TkUIAUIZJHW2upEBad Habits7.0231041.0False0.8080.89711.0-3.7120.00.03480.046900.0000310.36400.591126.0264.00
40451026171073Running Away482021-07-31VANO 3000, BADBADNOTGOOD, Samuel T. HerringMOVE_DOWNNaN2lDODk7inZnmUHbIjUnIwPRunning Away0.0111786.0False0.5800.6967.0-10.9101.00.04080.660000.8520000.14500.72193.6884.00
40451126171074Casualidad492021-07-31Sofía Reyes, Pedro CapóNEW_ENTRYNaN5FcT2TuosRkokjn3xyncERCasualidad54.0193112.0False0.7530.72010.0-4.2230.00.09870.383000.0000000.05540.840173.9064.01
40451226171075Pieces502021-07-31AVAIONNEW_ENTRYNaN5H95n43z0KFcXGCEc0ewe1Pieces68.0196413.0False0.7000.2714.0-13.3161.00.06200.872000.3220000.10000.198120.0154.01